Magnetic inspection of reinforcing steel using sensor array

ABSTRACT

A method of inspecting the reinforcing members in prestressed concrete beams by generating a magnetic field close to the beam and measuring the field by means of a Hall effect sensor located between the pole pieces of the magnet. A partial or total break in a reinforcing member produces an anomaly in the magnetic field, which in turn registers as a voltage &#34;spike&#34; in the output of the Hall effect sensor. The method further includes a method for enhancing the data by substantially reducing or eliminating the effects of transverse reinforcing steel or support members located at the site of a break in the prestressed member.

STATEMENT OF GOVERNMENT INTEREST

The present invention was made under Government contract and may be madeor used by or on behalf of the Government of the United States withoutthe payment of any royalties thereon or therefor.

BACKGROUND

About 25 years ago, a new bridge structural design using prestressedconcrete was introduced. In recent years, the use of prestressedconcrete bridge beams has been widespread and such design nowincorporates a variety of structural configurations. Prestressedconcrete bridge structural members are of two general types,pretensioned and post-tensioned. Current pretensioned constructiondesigns usually consist of 7-wire strands, on the order of 1/2-inch indiameter, arranged in a matrix on 2-inch centers and the strands aretensioned prior to pouring the concrete. Beams with pretensioned membersare usually made at a plant site because of the special fabricationfacilities and tooling required. In the case of the post-tensionedconfiguration, ducts, usually metal, are placed in a specified locationand configuration before the cement is poured; subsequently, thereinforcing strand, rod, or bar is inserted and tensioned, usually atthe bridge site, and grouting material is introduced to fill the spacebetween the reinforcing member and the duct. The load-carryingcapability of prestressed bridge structural members is directlydependent upon the strength of the steel reinforcement rods, bars, orstrands; hence, the integrity of this steel is of primary concern and isinfluenced by one or more of the following factors:

(1) Quality of manufactured reinforcement material--governed bydimensional tolerances, strength, ductility, metallurgical type flawssuch as voids or impurities, and mechanical damage such as nicks,gouges, etc.

(2) Corrosion deterioration as a result of field environment.

(3) Fracture failure as a result of over-stress (caused by loss ofsection due to corrrosion deterioration) or by impact loading (as aresult of construction or vehicular impact).

(4) Loss of bond between steel and concrete associated with corrosion ofpost-tensioned members due to voids in duct grouting collectingmoisture.

In recent years, there is conclusive evidence that deterioration of thesteel as a result of corrosion occurs; furthermore, such deteriorationcritically affects the structural strength. Currently used inspectionprocedures rely heavily on rust staining, cracking, and spalling of theconcrete as an indicator that a problem exists in the reinforcing steel.

However, deterioration and even fracture of the reinforcing member canoccur without being preceded by visual evidence on the external surfacesof the concrete members. For example, an elevated highway in a largecity supported on 192 beams presently has more than 21 bars suspected ofbeing fractured. Four such fractures have been confirmed. In this case,the presence of corroded and fractured post-tensioning bars wasdetermined only from (i) the projection of one end of the bars beyondthe end of the beam during a visual inspection, (ii) the loud noise madeby one of the bars when it broke, which was heard by people in the areawho reported it to the State, and (iii) a broken bar which extended farbeyond the end of the beam it was intended to reinforce, therebyinterrupting traffic. There are no cracks or significant rust stainsvisible on the exterior surfaces of these particular beams.

From an overall point of view the problem is extremely broad becausethere is a wide variety of structural designs and the mechanismscontributing to the decrease or loss of structural integrity arecomplicated.

OBJECTS OF THE INVENTION

Accordingly, it is an object of the present invention to provide amethod of inspecting the reinforcing members in prestressed concrete.

It is a further object to provide such a method which can be used oneither pretensioned or post-tensioned members.

It is a further object to provide such a method which can detect partialas well as total failure of the member.

It is a further object to provide such a method which produces a printedrecord of the results of the inspection.

It is a further object to provide a method of enhancing the raw datafrom an inspection to eliminate the effects of reinforcing members notof interest.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an overall view of an inspection system of the presentinvention.

FIG. 2 shows the cart and sensor assemblies.

FIG. 3 shows the location of the reinforcing members in the test beam.

FIGS. 4(a)-(d) show the effect of increasing fracture end separation ina 13/8 inch diameter bar.

FIGS. 5(a)-(d) show the effect of different flaw sizes in a 1 inchdiameter bar.

FIGS. 6(a)-(c) show the effect of different flaw sizes in a 1/2 inchdiameter 7 wire strand.

FIGS. 7(a)-(d) show the effect of position within the strand matrix.

FIGS. 8(a)-(c) show the effect of a transverse reinforcing member atvarying distances from a flaw.

FIGS. 9(a)-(f) show the signatures obtained at the end of a beam orscan.

FIGS. 10 and 10(a)-(f) show the results of data enhancement by thesubtraction process.

FIGS. 11 and 11(a)-(d) show the results of data enhancement by thesubtraction and correlation process.

FIGS. 12(a)-(c) show a typical flaw signature, a flaw algorithm, and acorrelation between the two.

FIGS. 13(a)-(b) show correlation coefficients as a function of B.

FIGS. 14 and 14(a)-(f) show the signature of a piece of wire scrap nearthe bottom of the beam.

FIGS. 15 and 15(a)-(f) show a comparison between side mounted sensorsand bottom mounted sensors.

SUMMARY

Briefly, the present invention is a method of magnetically inspecting areinforcing member in prestressed concrete. A magnetic field is passedthrough the member and measured with an array of Hall effect sensors;anomalies in the field indicate the presence of flaws in the member.Methods of enhancing the raw data, by subtracting the data obtained fromone sensor from that obtained from another sensor, to eliminate theeffects of transverse reinforcing members are also shown.

DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 shows an overall view of a prestressed concrete beam 10 having afractured prestressing element 12 within it that is to be inspected bythe magnetic field inspection system of the present invention.Electromagnet/sensor assembly 14, which comprises an electromagnet 16and a Hall effect sensor 18 mounted between the pole pieces ofelectromagnet 16, is mounted on cart 20 (see FIG. 2) that rides on rails(not shown) which are suspended from beam 10.

Electromagnet 16 comprises 5700 turns of 14 gage aluminum magnet wirewound around a soft iron core. A current of 2 amps DC is put through it,producing 11,400 ampere-turns of magnetization. It is necessary toproduce a magnetic field that extends to and is within all of thereinforcing members to be inspected, otherwise the members cannot beinspected. It is also necessary that the axis of electromagnet 16 beparallel to the members in order that the magnetic field be parallel tothem rather than cut across them.

Hall effect sensor 18 is a model FH-301-040 sensor manufactured by F.W.Bell, Inc., 6120 Hanging Moss Road, Orlando, FL 32807.

Cart 20 as shown in FIG. 2 comprises body 22 mounted on wheels 24 whichare driven by longitudinal drive system 26. Within body 22 is magnetplatform 28 which is mounted on guide rods 30; magnet platform 28 ismoved transversely of body 22 on guide rods 30 by transverse drivesystem 32. Cart 20 must be made wide enough to allowelectromagnet/sensor assembly 14 to be placed beneath the reinforcingmembers near the sides of the beam (members 1 and 10 in FIG. 3) in orderto inspect all of the members. When the sytem is to be used to inspect abeam, electromagnet/sensor assembly 14 is mounted on magnet platform 28and hooked up to the electrical system by connectors 34 and 36.

FIG. 3 shows the distribution of reinforcing members (rods or strands)within the test beam, which is a Texas Type "C" beam. There are 38longitudinal members as well as stirrups 40 which are used to reinforcethe web of the beam; there are many such stirrups along the length ofthe beam, but only one is shown. Of the longitudinal members, numbers21, 30, and 33 are permanently bonded into the beam; number 14 is a 15/8inch diameter flexible steel duct, number 17 is a 23/8 inch diameterrigid steel duct, and the rest are 9/16 inch inside diameter rigid PVCtubing. During testing a reinforcing member of known configuration (rodor strand, flawed or non-flawed) is put into one of these tubes or ductsand a magnetic "signature" of that member is recorded on a conventionalstrip chart recorder (not shown).

Operation of the inspection system is as follows. The rails whichsupport cart 20 are suspended from the beam to be inspected by anymeans, or they can be supported from the ground; the only requirement isthat they allow the cart to remain a constant distance from theunderside of the beam. The distance between the rails and the beam isthen adjusted to give the minimum practical gap between the top ofelectromagnet/sensor assembly 14 and the bottom of the beam beinginspected. Cart 20 is then moved longitudinally under the beam and theoutput of the Hall effect sensor is recorded on the strip chartrecorder. Since the output of the Hall effect sensor is a voltage thatis proportional to the magnetic field that exists at the sensor and thisfield is influenced by the steel within the prestressed members, theoutput is an analog equivalent of the magnetic field.

At this point it should be pointed out that interpretation of inspectiondata from a beam is practically impossible without the aid of aconstruction drawing of the beam which shows the approximate location ofall reinforcing members within the beam. As will be shown later, it wasfound that the signature of a stirrup or rebar chair (a small steelsupport used to hold a reinforcing member in place when the concrete ispoured) is similar to that of a flaw. Another time a flaw-like signaturewas found whose location did not correspond to any reinforcing membersor rebar chairs; upon closer inspection and partial excavation it wasfound to be caused by a scrap of the wire that was used to tie thereinforcing members together and which had not been removed from theform before concrete was poured. Without a construction drawing it wouldbe difficult or impossible to determine if a signature were from a flawor a transverse reinforcing member, unless the data enhancementtechniques shown later are used.

FIGS. 4(a)-(d) show the signature response from a 13/8 inch diameterhigh-strength steel bar with a simulated fracture and varying degrees ofseparation between the fractured ends. The data shown in FIGS. 4(a)-(d)are for the center line of the flawed bar 3.5 inches above the bottomsurface of the test beam being scanned; the clearance between the magnetpole faces (and Hall effect sensor) and the concrete surface of the beamwas approximately 0.5 inch. As can be seen, the record of the magneticfield is a straight line where there are no transverse reinforcingmembers or fractures in the prestressed members; where these exist theyproduce a signature which is an anomaly in the record in the form of avoltage spike or hump. Note that the signature from the simulatedfracture for 0.5 inch separation and greater can be readilydistinguished from those produced by the transverse stirrups.Furthermore, for the cases in which the simulated fracture is locatedbetween stirrups, and separations as small as 0.015 inches can bediscerned because of the large horizontal extent of the signature. InFIG. 4(a), the horizontal distance between the upward-going anddownward-going peaks of both the stirrup and fracture signatures hasbeen indicated. This separation between the peaks is proportional to thedistance between the sensor and the flaw and/or steel configurationcausing the signature, and is a parameter which can be used to identifythe depth of the element causing the signature. Note that the separationof the peaks for the stirrups is approximately one-third of that for thesimulated fracture; correspondingly, the lower arm of the stirrups isapproximately 1.5 inches from the bottom surface of the girder while thepost-tensioned bar is approximately 3.5 inches from the bottom surface.This peak separation feature of the signatures will be referred tothroughout since it is a parameter which can be extremely helpful in theinterpretation of inspection results. A comparison of correspondingsignatures in FIGS. 4(a)-(d) shows excellent repeatability even thoughthe bars were removed and replaced to set up the various flawconditions. Also, it is pointed out that stirrup signatures could bemonitored to assess possible deterioration in the stirrup regions and todetect missing stirrups.

Signature responses from varying degrees of reduction in cross-sectionalarea (simulation of loss of section due to corrosion) for a 1 inchdiameter high-strength steel bar are shown in FIGS. 5(a)-(d). FIG. 5(a)shows the response from a 50-percent reduction in area over a 1/2 inchlength in a 1 inch diameter bar; the insert shows a similar responsefrom a slightly lower percentage reduction in area for a 13/8 inchdiameter bar. In FIGS. 5(a)-(d), the bar centerline is approximately 3.5inches from the bottom surface of the concrete beam. Evaluation of thesignatures for the 20-percent area reduction for this concrete coveragecondition indicates a good probability for the detection of a 10-percentreduction in area provided adequate stirrup signature discriminationcould be developed. Discrimination between configurational steelartifact signatures and simulated flaw signatures is discussed later.

FIGS. 6(a)-(c) and 7(a)-(d) present results from simulated flaws in a1/2 inch diameter×7-wire strand arranged in a typical 2 inch by 2 inchmatrix. FIGS. 6(a)-(c) results for varying degrees of stranddeterioration with the flawed strand in the first row of the matrix (seepartial cross-sectional view of the girder at the upper left corner).Inspection of FIGS. 6(b) and (c) indicates that detection of a 14percent reduction in cross-sectional area would probably be marginal.The records in FIGS. 7(a)-(d) show a rapid decrease in signatureamplitude from the same size flaw for increasing depths of the flawedstrand in the matrix. FIGS. 7(b), (c) and (d) present flaw signaturedata from an 86 percent reduction in area over a 2 inch length (removalof six wires from the 7-wire strand over a 2 inch length) with thisflawed strand 2 inches, 4 inches, and 6 inches, respectively, above thebottom surface; unflawed strands adjacent to the flawed strand arepresent in all cases as indicated by the cross-section at the upper leftin each record. The results in FIGS. 7(a)-(d) indicate that detection ofan 86 percent×2 inch loss of section deeper than the first row of thematrix would probably be marginal.

The influence of signatures from reinforcement steel details, such asstirrups, holdowns, etc., on flaw signature recognition is illustratedin FIGS. 8(a)-(c). FIGS. 8(b) and (c) indicate that the field anomaliesfrom the combined steel and flaw configurations are essentiallycumulative. The combined effects can significantly modify the amplitudeand shape of the resulting signal; for example, note the greateramplitude and significantly reduced horizontal separation between theupward-going and downward-going peaks (peak separation) of the combinedstirrup and flaw signature in FIG. 8(c) as compared with that for theflaw signature only in FIG. 8(a). Importantly, the outstanding signaturefrom the simulated fracture (0.5-inch separation) illustrated in FIGS.8(a)-(c) for a concrete coverage of approximately 3.5 inches indicatesthat such a condition should be readily detectable. However, thepresence of other types of steel details as well as other complicatingfactors can significantly influence the interpretation of inspectiondata.

FIGS. 9(a)-(f) show records illustrating detection of deterioration(flaw condition) near the end of a beam which is resting on a bent suchthat an inspection scan to the end of the beam is not possible. The testbeam was made so that the reinforcing steel configuration in end A wasthat of the end of the beam and end B was that of the middle of thebeam; thus the results for end B simulate the results for the middle ofa real beam. This was done because the catenary duct is much closer tothe bottom of the beam at the middle than at the two ends and may have agreater effect on the magnetic field at the middle of the beam than atthe two ends. The results in FIGS. 9(a)-(f) indicate that the presenceof a significant flaw (such as a bar fracture with 0.5 inch separation)can be detected even though the sensor cannot be placed directly beneaththe flaw. Based on these results, it is estimated that a significantflaw can be detected when it is approximately 6 inches beyond the end oftravel of the cart.

After a series of tests with multiple sensors arranged in transverse andvertical arrays it was found that the optimum configuration was an arrayof four sensors in two rows, with the first row comprising three sensorsnearest the reinforcing members and the second row comprising one sensorbelow the middle sensor in the first row (see FIG. 10).

The entire array is between the pole pieces and the three sensors areflush with the pole pieces. Initially the horizontal and verticalspacing of the sensors was 1 inch; it was later found that a horizontalspacing of 2 inches provided better results. Obviously it would bepossible to use just a single sensor and make 4 passes down the beam foreach reinforcing member, with the sensor in a different location withrespect to the magnet for each pass, but this is too time-consuming.

In order to allow the data to be electronically enhanced andmanipulated, it was magnetically recorded in addition to being displayedon the strip chart. For this purpose a shaft encoder assembly (notshown) was mounted on the inspection cart to provide spatial-intervalsampling rather than time-interval sampling because the flaws arespatially distributed within the beam. Subsequent to data acquisitionand storage, selected data were retrieved from mass storage andprocessed using several signal analysis routines. The results fromvarious processing techniques were then compared to determine whichapproaches were most promising for the various types of flaw and steelartifact signal recognition and interpretation problems.

The different signal processing techniques that were explored are:

(i) Fast Fourier Transform (FFT)--a discrete Fourier transform appliedusing digital techniques to characterize the frequency spectraassociated with a signal of interest. The thrust of this technique is tocharacterize the spectra associated with various signal types (stirrups,simulated flaws, artificts, etc.) to determine if there are uniquespectral characteristics asociated with each.

(ii) Differencing--point-by-point subtraction of signals from differentscan tracks. The thrust of this technique is to enhance flaw signalswith respect to those of stirrups and/or artifacts; and

(iii) Correlation--a process similar to using a signal shape of interestas a template and sliding it along a scan record to determine if thetemplate is a good match to any given region in the scan record. A goodmatch between the template signal and a portion of the scan recordindicates a high probability that the source of the template-type signalis present in the beam at a location corresponding to the matchinglocation in the scan record. Mathematically, the correlation processquantitatively assesses the degree of shape matching between twosignals. The correlation process is performed automatically by the logicunit under the command of the operator. The thrust of this technique isto quantitatively assess the degree of similarity between a selectedsignal or algorithm (mathematical representation of a flaw, a stirrup,an artifact or other type of signal) and those signals present in anunprocessed or previously processed scan record.

Combination of two or more signal processing techniques can providepowerful tools for signal enhancement and recognition. For example, acombination of differencing and correlation is very effective as will beshown later. Additionally, the use of knowledge about the steelconfiguration within the beam under inspection (from constructiondrawings) is very important in the application of any signal analysismethod. The recognition of the symmetry or approximate symmetry ofelements within the beam can provide guidance for selecting the specificdifferencing and correlation analyses. The recognition of certain typesof elements from signal features in a set of scan data could providegreat insight into the most fruitful analysis technique(s) to use.

Frequency spectra characterization of typical flaw signals and stirrupsusing the FFT approach showed little difference in response betweenthese two types of signals. Furthermore, the results of correlationanalysis, to be discussed later, have confirmed the similarity betweenstirrups and flaw signals. Accordingly, the bulk of the signalprocessing investigations were directed at the effective combination ofthe sensor array with differencing and correlation signal processingtechniques. The added capability provided by use of a side sensor wasalso investigated. Typical examples of these techniques, along with theresults obtained, are presented in FIGS. 10, 10(a)-(f), 11 and11(a)-(d).

As explained above, differencing consists of the point-by-point digitalsubtraction of signatures from different channels and/or scan tracks.The object of this process is to enhance a flaw signatures more thanstirrup and/or artifact signatures through a priori knowledge of thesteel configuration and/or symmetry. FIGS. 10(a) and (b) show dataacquired from Channels 1 and 2 by scanning a strand with a medium sizedflaw, a 1/4 inch gap in 6 wires of a 7-wire strand, for a strandposition near the center of the beam (see FIG. 10 for strand location).The location of the flaw is indicated by the arrows in each signature.FIG. 10(c) shows the results of point-by-point subtraction of Channel 2from Channel 1. Note, by comparison with the upper two signatures, thatafter differencing the stirrup signals are almost eliminated and theflaw signal is more recognizable. It is pointed out that in thisdifferencing process, no adjustment of the signature amplitude foreither channel was made; accordingly, the normalizing factor is equal toone.

FIG. 10(e) shows the result of subtracting Channel 2 from Channel 1after multiplying Channel 2 by the normalization factor shown (FIGS.10(d) and (f) will be reviewed after a brief discussion of thecorrelation process). The normalization factor was obtained fromdividing the peak-to-peak amplitude of the fourth stirrup (referencestirrup) signal from the left in the Channel 1 record by the amplitudeof the same stirrup signal in the Channel 2 signature. Note that theresults of this normalization process are very similar to those obtainedfrom an unnormalized, direct differencing of the two channels(previously shown by FIG. 10(c)).

Another example of the differencing process for the same strand locationand flaw, previously presented in FIGS. 10(a)-(f), is shown in FIGS.11(a)-(f). FIGS. 11(a) and (b) were obtained from Channel 1 and Channel4, respectively, for a scan directly beneath the flawed strand. In thiscase, the Channel 4 sensor element was located directly beneath theChannel 1 sensor element but 1 inch further beneath the beam.Accordingly, the signature should appear to be similar but somewhatreduced in amplitude. FIG. 11(c) presents the results of point-by-pointsubtraction of Channel 4 from Channel 1. In this case, note how muchmore outstanding the flaw signal is with respect to those from thestirrups.

In addition to the differencing approach, another powerful technique,mentioned earlier, is that of correlation analysis.

Correlation analysis is a mathematical process which quantitativelyassesses the goodness of fit (or the degree of shape matching) betweentwo signals or signatures. The quantifying number describing the fit iscalled the sample correlation coefficient, R. The correlationcoefficient can be computed automatically, using the logic processingsubsystem according to the following equation, where R is the samplecorrelation coefficient for signatures "a" and "b": ##EQU1## x_(i) isthe set of n data points describing the amplitude of the digital samplesfor signature "a" (i.e. the reference flaw)

y_(i) is the set of n data points describing the amplitude of thedigital samples for signature "b" (i.e. the suspected flaw)

x is the mean of the x data points

y is the mean of the y data points

n is the number of data points in each set

The range of values possible for R is ±1. Correlation of two identicallyshaped signatures yields R=+1. Correlation of two signatures identicalin shape but with reversed polarities yields R=-1. An important propertyof the correlation function above is that it is independent of theabsolute amplitudes of the signatures being correlated.

The above correlation computation consists of the multiplication of twomatrices, each matrix consisting of a set of numbers representing thelocation and amplitude of the digitized data points from a signal orsignature. One axis in each matrix represents the location of eachspatial sampling interval and the other axis represents the digitalsignal amplitude at each sampling interval. For the present application,a 101-point matrix representing a reference flaw signal was correlatedagainst a 590-point matrix which represents an entire scan signature(such as FIG. 10(a)). The calculation is carried out by mulitplying the101-point matrix representing the reference flaw signal by the matrixrepresenting the first 101 data points in the scan signature startingfrom the left, i.e. starting at data point 1 and extending through datapoint 101. Subsequently, the 101-point matrix representing the referenceflaw signal is multiplied by the matrix representing the next 101 datapoints in the signature starting with data point 2 and extending throughdata point 102. This process is continued point by point along the scansignature and results in 490 correlation coefficients which are thenplotted.

An example of a correlation coefficient plot is shown by FIG. 10(d);this is based on FIG. 10(c), which is channel 1 minus channel 2. Thegreater the correlation coefficient value, the better the match betweenthe flaw signal being used to interrogate the scan track and thatsegment of the scan signature which corresponds to the location of thecorrelation coefficient peak, and the higher the confidence that a flawexists. The maximum correlation coefficient value in a correlation plotis indicated by the cursor (an inverted V) near the baseline pointingout the peak, such as that associated with peak 2 in FIG. 10(d). Thepresent processing program prints out the value of only those peaksgreater than 0.700; however, any other value could be used.

In the case of FIG. 10(d), notice that the value of the correlationcoefficient peak correspoinding to the flaw location, 0.861, is slightlyless than that of the maximum peak 2, 0.881, which corresponds to astirrup. This would result in a stirrup being identified as a flaw, orwould at best result in the identification of two possible flaws. Toattempt to overcome this the normalizing factor was increased from 1.000to 1.150 and the correlation coefficients were again calculated as shownin FIG. 10(f). The correlation coefficient peak associated with the flawis the highest shown in the plot, but the value of peak 2 issufficiently close to it to raise doubts.

As a final step the correlation coefficients were calculated for channel1 minus channel 4, shown in FIGS. 11(a)-(d). Record D clearly andunambiguously shows the flaw, with no other peaks as high as 0.700. Thusit is clear that the best flaw detection method is to use two sensorslined up directly beneath the member being inspected, subtract theoutput of the farther one from the output of the nearer one, and thencalculate the correlation coefficients as shown.

Up to this point, a typical flaw signature has been considered for useas the interrogating correlation function. It is possible, however, touse an algorithm to represent the flaw signature and such a procedurefacilitates modification of the flaw signal shape which can be shown tobe related to certain flaw propereties and to the depth of the flawwithin the beam. FIGS. 12(a)-(c) present graphs of a typical signature,an algorithm (and its equation), and a plot of the correlationcoefficient for the two signatures. The similarity between a graphicalpresentation of the algorithm equation and that of a typical flawsignature (from a 1/4-inch gap in 6 wires of 7-wire strand) is obvious.

The mathematical relationship for the algorithm in FIG. 12(b) was basedon a theoretical model for the magnetic field perturbance from a sphereof permeability u_(o) buried in an infinite volume of differentpermeability material, u. The two constants in the algorithm equationare A, which is related to amplitude, and B, which is a shape factor,usually referred to as peak separation. Peak separation is a parameterthat is associated with the distance between the sensor and the flaw;the greater the sensor-flaw distance, the greater the value of B. Thevalue of B may also be influenced by the length of the flaw if thedistance over which the flaw extends is significant compared to thesensor-flaw spacing (e.g. extends over a distance equal to or greaterthan the sensor-to-flaw spacing). The goodness of fit between thealgorithm and a typical flaw signature is confirmed in the fact that amaximum correlation value of 0.995 is obtained for a peak separation ofB=16. All correlation results presented were obtained using thealgorithm of FIG. 12(b); the value of B is indicated in each case. Thegraphs of correlation value as a function of B in FIGS. 13(a)-(b) fortypical flaws in a strand and in a bar were obtained by varying the Bvalue of the algorithm when correlated against a flaw signal having apeak separation of B=16 for the strand flaw and B=32 for the bar flaw.

An important property of the correlation function, i.e. that inversionof polarity between the interrogating or flaw signal and a signal in thescan signature will result in a negative value of the correlationcoefficient, makes it possible to eliminate the influence of disturbingelements such as wire scrap near the lower surface of the beam fromfurther consideration. For example, FIGS. 14(a) and (b) show theinfluence of a 11/2 inch long piece of 16 ga. iron "tie" wire on themagnetic signature from Channels 1 and 4 when scanned directly beneaththe wire scrap. Because of the greater proximity of the wire scrap tothe sensor of Channel 1 than to the sensor of Channel 4, the signalamplitude is greatly reduced on Channel 4 (see arrows in FIGS.14(a)-(f)). In addition, notice that the signal from the wire scrap isinverted in polarity; that is, the wire signal is first downward-goingthen upward-going as viewed from left to right while the signal from athen upward-going as viewed from left to right while the signal from astirrup is upward-going and then downward-going. Flaw signals have thesame polarity as stirrup signals, upward-going then downward-going asviewed from left to right (see FIGS. 4-7 where the flaw signal ispointed out by the arrow). FIG. 14(c) shows the result of differencingChannel 1 and Channel 4 --a prominent "opposite polarity" signal isobtained from the piece of wire mounted on the lower surface of thebeam. FIG. 14(d) is a plot of the correlation coefficient for thedifferenced signature; note that the maximum coefficient (without regardfor sign) corresponds to peak 2, from the wire scrap, but is negative insign. It should be noted that this wire scrap correlation was obtainedfor B=8, indicating it is very near the lower surface of the beam. FIGS.14(e) and (f) show a progressively smaller value for the correspondingcorrelation peak (pointed out by the arrows) for values of B=16 andB=32. Accordingly, it is possible to discriminate betweeen surface andnear surface artifacts and flaws and those deeper within the beam; thiscan be done by merely varying the value of B in the correlationcoefficient calculations to see which gives the greatest coefficient,the corresponding value of B providng the indication of depth (i.e.depth is proportional to the value of B).

A configuration wherein the sensor is placed against the side of thebeam was also tried. FIGS. 15(a)-(f) show the results of this placementcompared to those from placement against the bottom of the beam. In thiscomparison test, the magnet and sensor are aligned up directly below thecolumn which contains the flawed strand, or the sensor is on the leftside of the beam on the centerline of the row which contains the flawedstrand and the magnet remains below the column which contains the flawedstrand. When the flawed strand, is in the first row and first column asin records A the signals are approximately equal. When the flawed strandis moved to the column 1 row 2 position FIG. 15(b), the side sensorproduces a much clearer indication of the flaw since it is closer to theflawed strand than the bottom sensor. When the flawed strand is movedinto the second, third, or fourth row (see FIGS. 15(e)-(f)) the bottomsensor picks up the presence of the stirrups; the side sensor does not,since the stirrup is not between the sensor and the strand.

Note that FIGS. 15(a)-(f) are raw data; that is, they have not beendifferenced as in previous Figures nor have correlation coefficientsbeen calculated for them. It is obvious that the use of two side sensorsin the same horizontal plane, with the data manipulated as before, wouldproduce equally clear results. In applications where there aresubstantial numbers of stirrups or other transverse reinforcing members,the use of side sensors may be preferable to the use of bottom sensors.

What is claimed is:
 1. In the method of inspecting the reinforcingmembers in prestressed concrete by generating a magnetic field whichextends to the reinforcing members, obtaining an analog equivalent ofthe field by means of a sensor located within the magnetic field, andrecording the analog equivalent of all points along the length of themembers, the improvement which comprises: obtaining an analog equivalentof the magnetic field from a plurality of sensors located within saidmagnetic field and recording the output of each sensor separately in theform of a graph, said sensors being in a plane which is perpendicular tothe reinforcing members and arranged in an array of 2 rows, with thefirst row comprising three sensors nearest the reinforcing members andthe second row comprising one sensor below the middle sensor in saidfirst row.
 2. The method of claim 1 further comprising centering saidarray of sensors below the member to be inspected.
 3. The method ofclaim 2 comprising subtracting the output of one of the side sensors insaid first row from the output of the middle sensor in said first row ateach point along the length of the reinforcing member; and recording theresults in the form of a graph.
 4. The method of claim 2 comprisingsubtracting the output of said sensor in said second row from the outputof the middle sensor in said first row at each point along the length ofthe reinforcing member; and recording the results in the form of agraph.
 5. The method of claims 3 or 4 including comparing said graphwith the graph of a reference flaw at regular intervals on said graph;obtaining a number between +1 and -1 representative of said comparison;and making a graph of said numbers.